Reviews Sentiment analysis for collaborative recommended system

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Alia Karim Abdul Hassan Ahmed Bahaa Aldeen Abdulwahhab

Abstract

Recommended system nowadays is used to deliver services and information to users. A recommender system is suffering from problems of data sparsity and cold start because of insufficient user rating or absence of data about users or items. This research proposed a sentiment analysis system work on user reviews as an additional source of information to tackle data sparsity problems. Sentiment analysis system implemented using NLP techniques with machine learning to predict user rating form his review; this model is evaluated using Yelp restaurant data set, IMDB reviews data set, and Arabic qaym.com restaurant reviews data set under various classification model, the system was efficient in predicting rating from reviews.


 

Keywords

recommender systems, sentiment analysis, opinion mining, natural language processing, text classification.

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